Furthermore, neural networks have played a significant role in the flow control community [1]}.
The first attempt of supervised machine learning-based flow control was performed in 1997 by Lee et al. [2]}.
Their model was trained to learn the control input of the opposition control [3]} using only the wall-sensor measurement so as to reduce the friction drag.
Many of recent efforts have been devoted to reinforcement learning [4]}.
Rabault et al. [5]} applied the reinforcement learning to perform active flow control with two jets on a cylinder surface.
The extension of the technique to different Reynolds numbers was assessed by Tang et al. [6]}, which achieves significant drag reduction of 5.7%, 21.6%, 32.7%, and 38.7%, at \(Re_D = 100, 200, 300\)  and 400, respectively.
Although these efforts are still limited to laminar flow cases, the success here motivates us its extension to turbulent flows.
